1. Field of the Invention
This invention relates generally to supplier sourcing and more specifically to a system and method for configuring scoring rules and generating supplier performance ratings.
2. Description of the Background Art
A fundamental tenet of economics, both theoretically and practically, is that consumers of goods and services typically purchase those goods and services from suppliers. Many consumers, especially companies and governments, use multiple suppliers to supply the same goods and services. These consumers use multiple suppliers for many reasons, including (i) to avoid becoming too dependent on any one supplier, (ii) to address the fact that one supplier or a small number of suppliers may not be able to produce the desired amounts of goods or services or deliver those goods or services within the requisite time frame, (iii) to obtain competitive pricing, and (iv) to have the flexibility to use suppliers capable of fulfilling specialized orders or transactions.
Although beneficial for these reasons and others, using multiple suppliers also creates several problems for consumers. One of the more pressing problems is determining which supplier is best suited for a given order or transaction. To address this problem, a consumer often employs some sort of supplier rating system to rate its various suppliers, awarding the order or transaction to the supplier or suppliers with the highest rating(s).
Generally, conventional supplier rating systems frequently implement scoring rules that use information from past transactions involving the different suppliers being rated to generate a performance rating for each supplier. A typical scoring rule is a function of several supplier oriented performance metrics, examples of which include price, delivery time, supplier credit worthiness and aggregate business conducted with the consumer. For each supplier, the supplier rating system (i) scores the supplier with respect to each performance metric defined in the scoring rule, (ii) converts each of these performance metric scores into a unitless converted score as defined in the scoring rule so that performance metric scores of disparate units and ranges can be compared and/or combined, and (iii) combines the converted scores in a manner set forth in the scoring rule to generate an overall performance rating for that supplier. A consumer then compares the performance ratings of the various suppliers and selects the supplier with the highest rating.
More specifically, a conventional supplier rating system begins with a scoring rule that rates each supplier with respect to a set number of performance metrics. The consumer normally has the ability to assign a weight to each performance metric that indicates the relative importance of that performance metric to the consumer. Each weight usually reflects the percentage of the maximum possible performance rating for which the corresponding performance metric would account. The sum of all of the weights therefore typically equals 100%.
The scoring rule also defines how to convert the individual performance metric scores into unitless converted scores. The scoring rule often employs a conversion scheme that maps a range of unitless converted scores onto the range of possible performance metric scores. Where a given performance metric score falls within the range of unitless converted scores determines the converted score attributed to that performance metric score. For example, assume that price is the performance metric at issue and that $0 to $5 million is the range of possible prices. If the scoring rule defines 1 through 5 as the range of unitless converted scores, then the scoring rule may dictate that a supplier's price of $1 million warrants a converted score of 5, a supplier's price of $2 million warrants a converted score of 4, a supplier's price of $3 million warrants a converted score of 3, and so on.
In most cases, the scoring rule applies the same conversion scheme to each performance metric, meaning that the scoring rule maps the same range of unitless converted scores onto the range of possible performance metric scores for each performance metric defined in the scoring rule. Continuing with the previous example, assume that delivery time is the performance metric at issue and that 2 to 10 days is the range of possible delivery times. The scoring rule again would use 1 through 5 as the range of unitless converted scores and may dictate that a supplier's delivery time of 2 days warrants a converted score of 5, a supplier's delivery time of 4 days warrants a converted score of 4, and so on.
As described above, for each supplier being rated, the supplier rating system generates a score for each performance metric in the scoring rule and converts each such performance metric score into a converted score as defined in the scoring rule. The supplier rating system then computes a scaled converted score for each performance metric. The scoring rule normally defines the scaled converted score as the converted score for a given performance metric multiplied by the weight the consumer assigned to that performance metric. Lastly, the supplier rating system sums the scaled converted scores to generate a performance rating for the supplier. The consumer then compares the performance ratings of the different suppliers and selects the supplier with the highest score as the “best” supplier for the project or transaction at hand.
A significant drawback to using conventional supplier rating systems is that the performance metrics used to construct the scoring rules are predetermined or fixed, meaning that the developer of the system determines the performance metrics, not the consumer. The consumer usually is not allowed to customize or otherwise modify any of the performance metrics in the scoring rule. For example, the consumer typically is unable to add any performance metrics to the scoring rule, regardless of their importance to the consumer. Likewise, the consumer typically is unable to delete any of the performance metrics from the scoring rule, even if they are of little value to the consumer. Consequently, the scoring rule in a typical supplier rating system oftentimes does not include many of the performance metrics relevant to consumers, thereby leading to performance ratings that are not necessarily a reliable measure of supplier performance.
Another related drawback is that the number of performance metrics used in conventional supplier rating systems is small. More often than not, a small set of performance metrics fails to cover adequately the range of factors that consumers value. Again, the result is an overly simplistic scoring rule that does not necessarily measure supplier performance reliably.
Yet another drawback to using conventional supplier rating systems is that changing the weights assigned to the various performance metrics in the scoring rule is cumbersome. As set forth above, the sum of the assigned weights typically is 100%. As a consequence, when a consumer alters one weight in the scoring rule, the consumer also must alter at least one other weight to maintain a sum of weights equal to 100%. Further, the consumer frequently must alter several weights to maintain the desired relative distribution of weights among the performance metrics. The result is that running different iterations of a scoring rule by varying the weights often proves unwieldy to consumers.
As the foregoing illustrates, a need exists for a supplier rating system that has greater configurability and weighting versatility than conventional supplier rating systems.